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</li>
<li class="">
<a class="" href="/Trainer/Vaildation loop/">Vaildation loop</a>
<a class="" href="/Trainer/Validation loop/">Validation loop</a>
</li>
<li class="">
<a class="" href="/Trainer/hooks/">Hooks</a>
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Pytorch-Lightning/LightningModule/index.html
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</li>
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<a class="" href="../../Trainer/Vaildation loop/">Vaildation loop</a>
<a class="" href="../../Trainer/Validation loop/">Validation loop</a>
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<a class="" href="../../Trainer/hooks/">Hooks</a>
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Trainer/Checkpointing/index.html
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Trainer/Distributed training/index.html
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Trainer/SLURM Managed Cluster/index.html
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Trainer/Training Loop/index.html
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<li class="toctree-l3"><a href="#check-which-gradients-are-nan">Check which gradients are nan</a></li>
<li class="toctree-l3"><a href="#check-validation-every-n-epochs">Check validation every n epochs</a></li>
<li class="toctree-l3"><a href="#display-metrics-in-progress-bar">Display metrics in progress bar</a></li>
<li class="toctree-l3"><a href="#display-the-parameter-count-by-layer">Display the parameter count by layer</a></li>
<li class="toctree-l3"><a href="#fast-dev-run">Fast dev run</a></li>
<li class="toctree-l3"><a href="#force-training-for-min-or-max-epochs">Force training for min or max epochs</a></li>
<li class="toctree-l3"><a href="#force-disable-early-stop">Force disable early stop</a></li>
<li class="toctree-l3"><a href="#inspect-gradient-norms">Inspect gradient norms</a></li>
<li class="toctree-l3"><a href="#make-model-overfit-on-subset-of-data">Make model overfit on subset of data</a></li>
<li class="toctree-l3"><a href="#process-position">Process position</a></li>
<li class="toctree-l3"><a href="#set-how-much-of-the-training-set-to-check">Set how much of the training set to check</a></li>
@@ -126,7 +132,11 @@
</li>
<li class="">
<a class="" href="../Vaildation loop/">Vaildation loop</a>
<a class="" href="../Validation loop/">Validation loop</a>
</li>
<li class="">
<a class="" href="../hooks/">Hooks</a>
</li>
</ul>
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@@ -170,7 +180,8 @@
<div role="main">
<div class="section">
<p>The asdf</p>
<p>The lightning training loop handles everything except the actual computations of your model. To decide what will happen in your training loop, define the <a href="../../Pytorch-lightning/LightningModule/#training_step">training_step function</a>.</p>
<p>Below are all the things lightning automates for you in the training loop.</p>
<hr />
<h4 id="accumulated-gradients">Accumulated gradients</h4>
<p>Accumulated gradients runs K small batches of size N before doing a backwards pass. The effect is a large effective batch size of size KxN. </p>
@@ -198,13 +209,6 @@ trainer = Trainer(lr_scheduler_milestones=[100, 200, 300])
trainer = Trainer(print_nan_grads=False)
</code></pre>
<hr />
<h4 id="check-validation-every-n-epochs">Check validation every n epochs</h4>
<p>If you have a small dataset you might want to check validation every n epochs</p>
<pre><code class="python"># DEFAULT
trainer = Trainer(check_val_every_n_epoch=1)
</code></pre>
<hr />
<h4 id="display-metrics-in-progress-bar">Display metrics in progress bar</h4>
<pre><code class="python"># DEFAULT
@@ -214,6 +218,14 @@ trainer = Trainer(progress_bar=True)
<hr />
<h4 id="display-the-parameter-count-by-layer">Display the parameter count by layer</h4>
<p>By default lightning prints a list of parameters <em>and submodules</em> when it starts training.</p>
<hr />
<h4 id="fast-dev-run">Fast dev run</h4>
<p>This flag is meant for debugging a full train/val/test loop. It'll activate callbacks, everything but only with 1 training and 1 validation batch.
Use this to debug a full run of your program quickly</p>
<pre><code class="python"># DEFAULT
trainer = Trainer(fast_dev_run=False)
</code></pre>
<hr />
<h4 id="force-training-for-min-or-max-epochs">Force training for min or max epochs</h4>
<p>It can be useful to force training for a minimum number of epochs or limit to a max number</p>
@@ -221,6 +233,13 @@ trainer = Trainer(progress_bar=True)
trainer = Trainer(min_nb_epochs=1, max_nb_epochs=1000)
</code></pre>
<hr />
<h4 id="force-disable-early-stop">Force disable early stop</h4>
<p>Use this to turn off early stopping and run training to the <a href="#force-training-for-min-or-max-epochs">max_epoch</a></p>
<pre><code class="python"># DEFAULT
trainer = Trainer(enable_early_stop=True)
</code></pre>
<hr />
<h4 id="inspect-gradient-norms">Inspect gradient norms</h4>
<p>Looking at grad norms can help you figure out where training might be going wrong.</p>
@@ -241,9 +260,20 @@ trainer = Trainer(overfit_pct=0.0)
trainer = Trainer(overfit_pct=0.01)
</code></pre>
<hr />
<h4 id="process-position">Process position</h4>
<p>When running multiple models on the same machine we want to decide which progress bar to use.
Lightning will stack progress bars according to this value. </p>
<pre><code class="python"># DEFAULT
trainer = Trainer(process_position=0)
# if this is the second model on the node, show the second progress bar below
trainer = Trainer(process_position=1)
</code></pre>
<hr />
<h4 id="set-how-much-of-the-training-set-to-check">Set how much of the training set to check</h4>
<p>If you don't want to check 100% of the validation set (for debugging or if it's huge), set this flag</p>
<p>If you don't want to check 100% of the training set (for debugging or if it's huge), set this flag</p>
<pre><code class="python"># DEFAULT
trainer = Trainer(train_percent_check=1.0)
@@ -257,7 +287,7 @@ trainer = Trainer(train_percent_check=0.1)
<div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
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@@ -291,7 +321,7 @@ trainer = Trainer(train_percent_check=0.1)
<span><a href="../SLURM Managed Cluster/" style="color: #fcfcfc;">&laquo; Previous</a></span>
<span style="margin-left: 15px"><a href="../Vaildation loop/" style="color: #fcfcfc">Next &raquo;</a></span>
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<p>The lightning validation loop handles everything except the actual computations of your model. To decide what will happen in your validation loop, define the <a href="../../Pytorch-lightning/LightningModule/#validation_step">validation_step function</a>.
Below are all the things lightning automates for you in the validation loop.</p>
<p><strong>Note</strong> <br />
Lightning will run 5 steps of validation in the beginning of training as a sanity check so you don't have to wait until a full epoch to catch possible validation issues.</p>
<hr />
<h4 id="check-validation-every-n-epochs">Check validation every n epochs</h4>
<p>If you have a small dataset you might want to check validation every n epochs</p>
<pre><code class="python"># DEFAULT
trainer = Trainer(check_val_every_n_epoch=1)
</code></pre>
<hr />
<h4 id="set-how-much-of-the-validation-set-to-check">Set how much of the validation set to check</h4>
<p>If you don't want to check 100% of the validation set (for debugging or if it's huge), set this flag</p>
<pre><code class="python"># DEFAULT
trainer = Trainer(val_percent_check=1.0)
# check 10% only
trainer = Trainer(val_percent_check=0.1)
</code></pre>
<hr />
<h4 id="set-how-much-of-the-test-set-to-check">Set how much of the test set to check</h4>
<p>If you don't want to check 100% of the test set (for debugging or if it's huge), set this flag</p>
<pre><code class="python"># DEFAULT
trainer = Trainer(test_percent_check=1.0)
# check 10% only
trainer = Trainer(test_percent_check=0.1)
</code></pre>
<hr />
<h4 id="set-validation-check-frequency-within-1-training-epoch">Set validation check frequency within 1 training epoch</h4>
<p>For large datasets it's often desirable to check validation multiple times within a training loop</p>
<pre><code class="python"># DEFAULT
trainer = Trainer(val_check_interval=0.95)
# check every .25 of an epoch
trainer = Trainer(val_check_interval=0.25)
</code></pre>
<hr />
<h4 id="set-the-number-of-validation-sanity-steps">Set the number of validation sanity steps</h4>
<p>Lightning runs a few steps of validation in the beginning of training. This avoids crashing in the validation loop sometime deep into a lengthy training loop.</p>
<pre><code class="python"># DEFAULT
trainer = Trainer(nb_sanity_val_steps=5)
</code></pre>
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Trainer/Vaildation loop/index.html → Trainer/hooks/index.html
+12 -8
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@@ -8,7 +8,7 @@
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<title>Vaildation loop - Pytorch lightning Documentation</title>
<title>Hooks - Pytorch lightning Documentation</title>
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@@ -87,10 +87,14 @@
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Trainer/index.html
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@@ -159,22 +163,25 @@ trainer.fit(model)
<li><a href="Training%20Loop/#anneal-learning-rate">Anneal Learning rate</a></li>
<li><a href="Training%20Loop/#Check-gpu-usage">Check GPU usage</a></li>
<li><a href="Training%20Loop/#check-which-gradients-are-nan">Check which gradients are nan</a></li>
<li><a href="Training%20Loop/#check-validation-every-n-epochs">Check validation every n epochs</a></li>
<li><a href="Training%20Loop/#display-metrics-in-progress-bar">Display metrics in progress bar</a></li>
<li><a href="Training%20Loop/#display-the-parameter-count-by-layer">Display the parameter count by layer</a></li>
<li><a href="Training%20Loop/#fast-dev-run">Fast dev run</a></li>
<li><a href="Training%20Loop/#force-training-for-min-or-max-epochs">Force training for min or max epochs</a></li>
<li><a href="Training%20Loop/#force-disable-early-stop">Force disable early stop</a></li>
<li><a href="Training%20Loop/#inspect-gradient-norms">Inspect gradient norms</a></li>
<li><a href="Training%20Loop/#make-model-overfit-on-subset-of-data">Make model overfit on subset of data</a></li>
<li><a href="../Pytorch-lightning/LightningModule/#configure_optimizers">Use multiple optimizers (like GANs)</a></li>
<li><a href="Training%20Loop/#process-position">Process position</a></li>
<li><a href="Training%20Loop/#set-how-much-of-the-training-set-to-check">Set how much of the training set to check (1-100%)</a></li>
</ul>
<p><strong>Validation loop</strong> </p>
<ul>
<li>Display metrics in progress bar</li>
<li>Set how much of the validation set to check (1-100%)</li>
<li>Set validation check frequency within 1 training epoch (1-100%)</li>
<li>validation_step function</li>
<li>Why does validation run first for 5 steps?</li>
<li><a href="Validation%20Loop/#check-validation-every-n-epochs">Check validation every n epochs</a></li>
<li><a href="Validation%20Loop/#set-how-much-of-the-validation-set-to-check">Set how much of the validation set to check</a></li>
<li><a href="Validation%20Loop/#set-how-much-of-the-test-set-to-check">Set how much of the test set to check</a></li>
<li><a href="Validation%20Loop/#set-validation-check-frequency-within-1-training-epoch">Set validation check frequency within 1 training epoch</a></li>
<li><a href="Validation%20Loop/#set-the-number-of-validation-sanity-steps">Set the number of validation sanity steps</a></li>
<li><a href="Validation%20Loop/#check-validation-every-n-epochs">Check validation every n epochs</a></li>
</ul>
<p><strong>Distributed training</strong> </p>
<ul>
index.html
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